Prepared for presentation at "6th Conference on AE/MS
Activity in Geologic Structures and Materials" Pennsylvania
State University June 11-13, 1996.

PREDICTION OF EARTHQUAKES
WITH AE/MS? WHY NOT H.L. Dunegan
May, 1996

INTRODUCTION

Earthquakes account for more loss of life and property than
any other natural phenomena. In spite of this fact, and the fact
that we know why and how earthquakes occur, there is a great deal
of pessimism from both the scientific community and Government
agencies concerning one's ability to accurately predict earthquakes.
This pessimism is evidenced by the large amount of funds expended
in earthquake preparedness programs compared to the funds available
for research concerning earthquake prediction. The primary reason
for the lack of an earthquake prediction model is the inability
of low frequency surface mounted seismic instrumentation to detect
the higher frequencies associated with small fractures that occur
prior to a large movement of a fault. The high frequencies associated
with these small events are attenuated by the upper mantel and
never make it to the surface.

It is shown in this report that the use of Acoustic Emission
techniques for predicting failure in materials and structures
with the use of high frequency sensors can find parallel applications
in predicting movements of a fault in the earth. The primary difference
is simply a matter of scale Acoustic emission applications utilize
frequencies to 1Mhz, to detect events on the order of 1 micron.
Its parallel in geophysical applications would be the use of frequencies
of 1 Hz to detect events on the order of 1 meter. The primary
difference with this simple analogy is that most acoustic emission
applications involve detecting stress waves in plates. In this
situation stress waves due to crack growth from a one micron area
can produce plate waves of much lower frequency as the wave propagates
and therefore can be detected with sensors in the Khz range of
frequencies. Whereas a fracture from a fault producing a 1 meter
area would have a fundamental frequency of approximately 2Khz
and would not undergo the same type of dispersion and mode conversion
observed in plates. We know from experience that a 1Hz ground
seismometer cannot detect a 1 meter fracture for any practical
distance. The only hope of detecting frequencies of 1Khz or more
in the earth is to place very sensitive sensors in deep wells
and space them in a 5 to 10 kilometer grid in order to get adequate
coverage of an area of interest (such as downtown Los Angeles)

Once the higher frequency events due to small fractures occurring
from a fault are detected, it is proposed that the data be handled
in a fashion similar to that used for acoustic emission data from
man made structures. Therefore some discussion of the procedures
used for handling acoustic emission data will be presented, and
analogies drawn to their use for earthquake prediction.

BACKGROUND

Acoustic emission technology has been used for many years for
the testing of pressure vessels, piping and other man made structures.
The foundation for much of this testing can be traced to the research
performed by the author and coworkers at Lawrence Livermore Laboratories
over 25 years ago. This research was
able to establish a relationship between the Stress Intensity
Factor (K) at a crack tip and acoustic emission signals that would
allow failure prediction models to be established. Figure 1 (Dunegan
1969) shows the acoustic emission counts as a function of stress
intensity factor K for several single edge notch fracture toughness
specimens with different crack lengths. The specimens with long
cracks failed at low loads while those with short cracks failed
at higher loads as would be expected. The fourth power curve through
the data points shows that the stress intensity factor K is the
normalizing factor for the data.

Figure 2 (Dunegan 1971) shows acoustic emission data from a
linear compliance fracture toughness specimen of high strength
steel that has been hydrogen charged and tested under dead weight
load in a creep frame. This type of specimen
has been designed such that for a given dead weight load, K remains
constant and independent of crack length over several inches of
crack growth. A crack opening gage was also used to provide information
on the amount of crack growth occurring due to the hydrogen embrittlement
cracking occurring in the specimen.

Note that the summation of acoustic emission counts and the
crack opening gage data can be fit with a straight line for a
given value of the stress intensity factor K. When K is increased
(by adding load) both the acoustic emission rate and crack growth
rate increase but remain constant for that value of K.

Diffusion of hydrogen to areas of high stress near the crack
tip and subsequent crack extension is the mechanism responsible
for both the acoustic emission signals and the crack growth. It
was observed during these tests that quite periods would occur
without much crack growth, and then a lot of activity would suddenly
occur. The longer the quite period the more the activity when
it did occur. This can be observed in figure 2 from the acoustic
emission data between 50 and 60 minutes for a K value of 22.5.
Following a long period of small activity a rush of signals occurred
over a very short time period, which caused the AE data to match
up with the straight line drawn through the data points. This
phenomena is what one would expect from activity along a fault
in the earth. If one assumes that the fault movement is occurring
under constant displacement rate, it would be expected that uniform
movement would result in a constant signal rate due to many small
fractures. If an area within detection range of the transducers
begins to be locked uniformly over a large area, stress will be
uniformly built up over this area with a minimum of small fractures
until the stress intensity factor reaches a critical value. At
this time a domino effect will occur and a rush of signals will
be present due to failures occurring over the large area, resulting
in stress relief of that area.

An example of how crystalline materials behave under very high
pressures when subjected to constant displacement rate loading
is shown by figure 3 (Dunegan 1967 unpublished). In this example,
acoustic emission signals (counts/sec) were recorded in a frequency
range of 100-300Khz from a Quartzite specimen undergoing confining
pressure of 3.5 Kbar and axial load. Increasing strain results
in increasing activity of the acoustic emission signals then a
sudden drop in activity occurs. The
drop in activity corresponded to a drop in load from the test
machine. The test machine was operating in a constant displacement
rate mode and therefore as displacement continued, stress buildup
again occurred in the specimen, followed by another drop in load
and acoustic emission activity... This type of failure mode called
"Stick Slip" is common in hard crystalline materials
under confining pressure such as Quartzite. It's interesting that
the acoustic emission rate where load drops occurred for each
instance in figure 3 had approximately the same value. One could
therefore predict from the acoustic emission data for this specimen
when the load drops would occur. The reason for the drop in load
and subsequent drop in acoustic emission activity is due to the
constant displacement rate of the crosshead in the stiff test
machine. The sudden slip occurring in the specimen relieves the
stress, and therefore stops the permanent deformation process
until enough displacement is again imposed to cause further deformation
to occur. This is an example of what is happening at the interfaces
of a fault in the earth. Fault movement is a constant displacement
rate process. If blockage occurs, the same deformation processes
and build up of stresses as shown in figure 3 occurs resulting
in stress waves having a very broad frequency bandwidth. If we
had also used a low frequency sensor during these tests its primary
response would have occurred when the load drop occurred. This
is analogous to the present use of low frequency sensors at the
surface of the earth. They record the stress drops after they
occur but give no information of the deformation processes leading
up the stress drop.

FAILURE PREDICTION

A model for failure prediction utilizing acoustic emission
data has been developed (Dunegan 1988) for man made materials
and structures. It has been observed that materials or structures
containing cracks that are loaded to failure, such that failure
occurs below general yield exhibit a power law increase in activity
prior to failure (see figure 1). Dunegan found that curve fitting
routines could be used with special software algorithms to recognize
the change in slope corresponding to imminent failure of a specimen
or structure. The procedure involves first fitting the summation
of acoustic emission counts with a 6th order polynomial, and taking
the first derivative of the data. The slope of the cumulative
amplitude distribution of the signals (b) is then calculated.
The first derivative values for each data point is then divided
by (b). The resulting factor for each data point defined as (Zfactor)
is again fit with a 6th order polynomial as the test proceeds
and first and second derivatives are calculated from the fitted
curve. The following algorithm is applied to the data in real
time.

if Zn>Zn-1>Zn-2 and 2nd derivative >=0 then alarm

Acoustic emission data from several sources in the literature
were replotted and curve fit and the above algorithm applied.
Table 1 lists the references, type of test and percent of failure
where an alarm was indicated by the model.

REFERENCE

TYPE OF TEST

%OF FAILURE

Harris-1974

Low cycle fatigue 7075-T6

79%

Harris-1984

High cycle fatigue-Rotor Steel

74%

Harris-1984

High cycle fatigue-Rotor Steel

80%

Harris-1972

Fatigue of Wire Rope 40dB gain

96%

Harris-1972

Fatigue of Wire Rope 60dB gain

87%

Komura-1979

Combined SCC and fatigue-304SS

67%

Dunegan-1971

Hydrogen Embrittlement Cracking

90%

TABLE 1

Percentage of failure that an automatic alarm
would sound utilizing the failure model presented in this report.

Failure Model Discussion

One might tend to argue that since the first derivative of
the cumulative counts as a function of time or cycles is simply
the count rate and counts per cycle, why not have a counter give
you these values as the test proceeds and forget about the complexity
of curve fitting? This will not work for the following reason:
The smoothing provided by curve fitting is needed for a machine
to unambiguously decide that a trend is developing. Cracks do
not grow in a smooth uniform fashion but by jumps and steps (see
figure 2). If one were simply to record the count rate or counts
per cycle the large jumps in the data throughout the test would
cause alarms to constantly occur. The same augments hold for applications
to activity from a fault. One would expect from time to time to
see swarms of activity created by small blockage at a particular
station, but in the case of earthquake monitoring one needs to
see the trends in adjacent stations in order to accurately predict
whether or not large movements (over several stations) is imminent.
This is why it is important in the applications to earthquake
monitoring to have adjacent stations equally spaced, and restrict
the data base for a particular station to a limited range through
frequency, spatial or amplitude ratio filtering.

Another argument one could make concerning the model applied
to earthquake monitoring is the number of data points used for
the curve fitting and the time interval between data points. We
have found that a minimum of 20 data points is needed for obtaining
a good fit to the data and a maximum of 40 data points was used
to curve fit the data in table 1. It is anticipated that adding
the cumulative data on a daily basis from each station would be
adequate, after 40 days the data would then be averaged back to
20 data points and fitting resumed on a daily basis until 40 data
points were accumulated. If the same type of accuracy can be obtained
as that in table 1, and a single station alarmed, a few days warning
of a minor earthquake would be indicated. If the alarm occurred
in a remote area it is unlikely that any damage would result.
On the other hand if say three adjacent stations alarmed within
a few hours of each other this could indicate a much larger anticipated
movement of the fault. If these three stations are in a populated
area warnings should be sent out that a large earthquake is likely
in the next few hours or days.

In order to set up an effective monitoring system all of the
stations should be capable of digitizing the AE/MS data and transmitting
the data over a digital network to a central processing center.
In this manner a predictive model can be applied to all stations
and combinations of adjacent stations, and in the event of an
earthquake in the network, one should be able to see the immediate
results of stress redistribution and its effects at each station
in the network.

HIGH FREQUENCY EARTHQUAKE MONITORING

The data in figure 3 shows that high frequency stress waves
are generated by irreversible deformation processes in rock like
materials. Earth quakes along strike slip
faults such as the San Andreas and San Jacinto fault in Southern
California typically occur at depths ranging from 5 to 20 km.
The high frequencies present due to deformation processes are
attenuated severely before reaching the surface and are not detected.
One can improve the detectability of the higher frequencies by
placing instrumentation in deep wells.

Abercrombie (1995) is the first to the authors knowledge to
obtain higher frequency data by using a very deep well. Experiments
were conducted at 2.4km in depth at Cajon Pass near the San Andreas
and San Jacinto faults using a triaxial geophone with a bandwidth
from 1 to 200hz. The signal to noise ratio of the earthquakes
recorded at this depth showed a vast improvement over the same
earthquakes recorded at the wellhead as evidenced by figure 4
which shows a comparison of the data from a 1.7ML earthquake occurring
at 10km distance at 2.4 Km depth with that obtained from a sensor
at the wellhead. Over a period of 2 years Abercrombie (1995) recorded
several thousand tectonic earthquakes. Approximately 90% of these
were too small to trigger the Southern California Seismic Network
(SCSN) or to be observed above surface noise by the sensor installed
at the wellhead. During this monitoring period hammer blows were
made to the wellhead and were not detected by the sensor at 2.4
Km. This further illustrates the value of obtaining data at great
depths. This data illustrates the feasibility of placing deep
well sensors throughout an area such as the Los Angeles basin
without interference from traffic and other surface noise. Making
measurements at this depth is not an easy matter. Manov (1996)
reported that temperatures of 105 C and pressures of 26 MPa were
present at the 2.4km depth. This was the primary reason for selecting
a passive sensor as opposed to an active sensor which would require
supplying power from the surface. In November of 1993 the 2.4
km sensor was removed and replaced with 2 sensors, one at 1.5km
and the other at 3km (Manov 1996). At 1.5km the temperature was
reported at 65 C, and pressure at 15MPa. At the 3km depth the
temperature was 120 C and the pressure 29MPa.

FAULT MODELING

The traditional thinking concerning failure along a strike
slip fault is that friction stresses create enough contact stress
to cause shear failures to occur parallel to the strike on one
of the plates. From a fracture mechanics viewpoint it appears
that if an obstacle is encountered such that the two plates lockup
at one point and the fault surfaces on one side of the blockage
remains fairly stress free, a mode II failure condition exists.
As stress continues to buildup due to displacement of one of the
plates the stress intensity factor K becomes critical and fast
fracture is initiated. One could also develop a model for the
same condition as above that would predict that a mode I (tension
perpendicular to crack surface) might also exist in the vicinity
of the blockage of a strike slip fault. We will start this development
with the following assumptions:

1. The fault is restricted from slipping along a finite section.

2. One plate is moving while the other is stationary.

3. At a depth of 10 to 15 kilometers viscoelastic flow of material
continues in the moving plate according to Tse and Rice (1987).

4. The plate is continuing to move upstream and downstream
of the blocked section.

5. Surfaces of the fault in contact below the elastic region
are only able to transmit short range elastic shear stresses due
to the viscoelastic nature of the material in contact in this
region.

6. The blocked section is prohibited from moving by frictional
forces or geometric obstacles.

Figure 5a shows a plan view of a strike slip fault with the
stationary plate being east of the moving plate, with the moving
plate moving north at a fixed rate. Three sections of the fault
are shown with movement occurring in section I and III, reacted
by a blocked section II.

Figure 5b shows a section just inside the west portion of the
fault line, with the forces created by the slip occurring on both
sides and below the blocked section shown by small arrows, with
slip indicated by large arrows. Since the plate is continuing
to move on the south side of the blocked section, compressive
stresses will be created at the boundary between section II and
III. This will cause compressive strain energy to be accumulated
in both sections II and III

The tension forces seen acting on the blocked section arise
from the continuing movement of section I relative to section
II. These forces must be zero at the surface, otherwise cracks
would form at the surface at the interface of section I and section
II. Since no cracks of this type are usually observed, one way
of meeting these boundary conditions is for tension cracks to
form at the interface of the elastic and viscoelastic region and
near the fault surface's as shown by figure 5b.

Figure 5c shows a section taken through the east side of the
fault near the fault line. The forces acting on the blocked section
are due to elastic shear stresses transmitted across the boundary
due to friction stress and/or obstacles mentioned previously.
These stress tend to move the blocked section north, creating
compressive stresses between boundaries I and II, and tension
stresses between boundaries II and III. The movement of the blocked
section creates shear forces at the interface between the elastic
portion and viscoelastic material below the blocked region. These
forces would tend to create tension cracks at the southern boundary
of this plate. Note that there is no viscoelastic flow occurring
in figure 4c, only reaction shear forces. This is due to the assumptions
2 and 5 given previously.

Tension Cracks

Rocks are very weak in tension, especially when saturated with
gases under very high pore pressures. Cracks forming at 10 to
15 kilometers in depth with temperatures of 300 C could produce
rather dramatic results. The vacuum initially created by the formation
of a crack would cause diffusion of gases and water vapor to this
region in an explosive manner, which could cause spalling of material
from the crack surfaces. Once an opening is available, the viscoelastic
material at the interface would tend to flow into and plug the
crack. As pressure continues to build along the crack surfaces
additional crack propagation could occur until the pressure inside
the crack comes into equilibrium with the pore pressure. Any water
vapor entering the crack will cause stress corrosion at the crack
tip, lowering the fracture toughness and stimulating additional
crack growth. In addition convection currents would be setup that
would raise the temperature of the crack tip, which could cause
creep effects at the crack tip. The new surface area created by
the cracks would lower the surface area resisting the movement
of the blocked section along the fault line. As new cracks form
parallel to the first crack due to a local decrease in pore pressure
and continuing tensile forces due to viscoelastic flow along the
boundary, the shear stresses along the fault will be relieved
enough for a seismic event to occur.

When a portion of the blocked section slips, the sudden release
of shear stress along the fault will cause the cracks to close,
and the tension forces will be transmitted to the interface between
the slipped region, and the region remaining blocked. In addition
the gases in the crack will suddenly be pressurized to a very
high-pressure due to this closure. These high pressure gases will
cause additional mode 1 tensile forces to be present at the crack
tip. This could cause additional crack propagation until equilibrium
can be established by diffusion. The high pressure gases will
also be forced into the gouge, providing a lubricating effect.

When blockage first occurs in a fault the seismic event mentioned
previously should occur very deep and on the north end of the
blocked section due to the higher tensile forces created by the
viscoelastic flow not present on the east side. After a seismic
event occurs, the portion of the blocked plate that has slipped
will be dormant for a period, and the blocked portion south of
this section will to tend to crack until a seismic event occurs
in this region. It is postulated that a large earthquake produced
by movement along the total length of the strike of the blocked
section will be preceded by a progression of small events preceding
south on the plate side and north on the continent side due to
decoupling created by tension cracks as proposed. The rate of
occurrence of these events and their magnitude should increase
prior to a large movement along the strike. This increase in rate
and amplitude is one of the basis for failure prediction model
proposed in an earlier section of this report. The other is the
shear failures that will occur along strike due to a sudden movement
of one section of the fault.

Supporting Observations

It has been reported by Rikitake, 1976 (Internet) that the
ratio of the P wave velocity to the S wave velocity decreases
prior to a large earthquake by approximately 10% and returns to
normal immediately before and following the earthquake. The diffusion
of gases into the crack and subsequent contact of the crack surfaces
to ground water could account for the presence of radon gas in
deep wells reported prior to an earthquake. If hydrocarbons are
present in the chamber created by the crack, the high pressures
generated during a seismic event could possibly force these hot
gases to the surface where ignition would occur. This could account
for the "earthquake lights" reported by many observers
during an earthquake. The presence of hydrocarbons in gaseous
form under such high pressures and temperatures could produce
a higher chain molecule through polymerization which would account
for the presence of petroleum bearing rocks in regions of high
fault activity. These cracks could also account for the decrease
in electrical resistance of the earth prior to a large earthquake.

Acoustic Emission Events

If the above model is correct, and tension cracks develop for
the reasons given, high frequency acoustic emission signals will
be generated prior to the seismic event. To
test the model the optimum placement of acoustic emission sensors
would be on the northern portion of the west blocked section as
shown by figure 5, and on the southern portion of the eastern
blocked section. Placement of the sensors in deep wells based
on an isotherm of 100 C or less should allow measurement of the
high frequency portion of the P and S wave generated due to small
fractures occurring prior to a large movement of the fault. From
considerations of the P wave velocity in granites and assuming
that a crack propagates at 1/2 the shear velocity, a 1 meter jump
in crack length would create an acoustic emission signal in the
2 kilohertz range. It will be shown that this high frequency signal
can be detected at a distance of 10 kilometers in homogeneous
rock under high pore pressure.

Attenuation effects

The 100Khz and higher data shown in figure 3 would not be able
to propagate 10km and still be detected, even though the Q of
granite at depths of over 1km is very high. An expression for
calculating the displacement at the surface from a fracture along
a fault line, including the effects of attenuation was derived
by Konamuri (personal communication) as follows: <>

(1):

Using values from Abercrombie (1995), Q=1000, c=6 km/sec, stress
= 100bars, f = 200hz, density = 2.8 g/cm3 and r = 10 kilometers,a
displacement of 2.5E-01 microns is calculated which is within
an order of magnitude of that observed by Abercrombie at the 2.4
km depth (figure 3) for a 1.7ML event at 10 km distance. The unknown value in the above equation
is the stress. Additional calculations were done increasing the
stress value until displacements comparable with figure 3 were
obtained. The frequency was then increased in order to measure
the effects on the displacement values at the 10 km distance.
Figure 7 shows the displacement values obtained to frequencies
up to 1,700Hz, showing that a displacement of 1 picometer would
be present at the 2.4 km depth used by Abercrombie (1995). A value
of 1 picometer was chosen as the stopping point for the data in
figure 7, since this is the value of displacement that can be
easily obtained with present transducer technology.

Calculations were performed using equation 1 and the conditions
for figure 7, to determine the boundaries on displacement and
frequency to produce 1 picometer displacement at the borehole.
The results of these calculations are shown in figure 7a. It appears
from the data that an order of magnitude decrease in distance
will allow an order of magnitude increase in the monitoring frequency.
These results show that it may be possible to monitor the high
frequency deformation processes due to stick-slip shown by figure
three, up to a distance of 1 km.

Historically strong motion transducers have been used to measure
seismic events. In order for these devices to operate, the container
housing the device must be induced to move by the event to be
measured. It is doubtful that 1 picometer of movement at the interface
between the granite wall and the sonde in a borehole containing
such a device would induce enough movement at 1700 Hz to create
enough signal to be measured from a velocity or acceleration transducer,
especially if the deep well contains a steel casing between the
wall and sonde. It appears that a more sensitive method of measuring
these small high frequency events would be to measure the displacement
of the wall of the well directly.

This could be accomplished by mounting a displacement transducer
on a stainless steel waveguide designed to penetrate the wall
of the sonde with "O" ring seals, and make direct contact
with the granite. A pointed carbide tip on the end of the waveguide
would assure sufficient coupling to the granite. A schematic of
such a device is shown by figure 8. Once the Sonde is in the hole
either a spring release or small motor would drive the waveguides
through the wall until they made contact with the granite. The
symmetrical arrangement shown in figure 8 for north, east, south,
west and the vertical waveguides would center the Sonde and transmit
the first P wave arrival from an event directly to the first transducer
struck before the wave interacts with the Sonde. Once
this interaction occurs the fidelity of the signal from all transducers
will be somewhat compromised. This is one reason that the first
hit channel along with the time of the hit is captured and recorded.
This information along with the same type of information from
adjacent boreholes will be used to accurately locate the source
of the signal. One can also measure the time difference between
the P and S wave to compute a hypocentral distance, but this calculation
will not be as accurate as using the first arrival P wave at several
stations.

Alternative Method

Deep boreholes are likely to be filled with water. An alternative
method of utilizing high frequency detection is to place high
frequency sensors at even intervals in the water column and utilize
the waveguide properties of the water column. Cross correlation
of the direct received signal at each transducer with the delayed
signal in the water column (due to slower velocity in water) would
allow the location of a seismic event to be calculated. Another
possibility would be to bond a high frequency sensor to the inside
wall of the sonde and record the high frequency dilational waves
transmitted through the water into the sonde.

DATA ANALYSIS

Assumptions

1. That the configuration shown by figure 8 is installed in
boreholes with an isotherm of 100C, or less spaced in a square
grid of 10 kilometer between stations.

2. Instrumentation is utilized that will digitize and store
all data at a 50Khz sampling rate.

3. A master clock signal is available to link all stations.

4. Each station has the necessary software to locate a source
by utilizing data from four adjacent stations. Spatial filtering
of the data will limit locations for analysis to seismic events
only occurring in its sphere of influence (5 km radius approximately
10km depth). a. Software to be written to accumulate a separate
database on more than 5 events (cluster) occurring within a 100
meter distance from each other.

4. The summation of amplitude, counts or energy from all events
within the sphere of influence will be tabulated and graphed as
a function of time. a. Separate graphs will be made from areas
showing clustering.

5. Tabulations and graphics will be transmitted to the central
station on a daily basis.

Central Station

The central station will curve fit each database transmitted
from the individual stations and apply the failure algorithm mentioned
previously. Special attention will be paid to the following.
1. Are any stations showing alarm status?
a. Is any clustered data at a station showing alarm status? b.
Is the data as a whole showing alarm status?
2. Are any two or more stations showing exceptionally quiet periods?
3. Are any two or more stations as a whole showing alarm status?

Figure 9 shows an example of two conditions that might exist
for data from 5 adjacent stations. 9A shows a "healthy"
condition for the faults nearby in that the activity is constant
over time indicating that stress relief is occurring uniformly
over all stations. On the other hand 9B shows a situation that
should be cause for concern. Three of the stations adjacent to
each other are showing long quite periods, which indicate that
strain energy is being accumulated on all three stations. If one
were to observe a pickup in activity on one station, followed
by a pickup on another, followed by a pickup on the third within
a matter of a few days, this increase in activity on all three
would probably create an alarm condition at all three stations.
This is an indicator that a large movement covering 30 km is likely
and a warning should be sent out if these stations are in a populated
area.

Discussion

The primary value of having predictive capability for earthquakes
is to give people some warning so that they can find shelter,
or remove themselves from an area where structural failure could
be life threatening. A joint program between The California Institute
of Technology (Caltech) and the United States Geological Survey
(USGS) will begin testing real-time earthquake monitoring with
a small network of 20 digital seismometers linked to the Pacific
Bell digital telecommunications service called Frame Relay (Internet
1996). The justification for the system is to cut as many as 30
minutes off the time needed to collect, calculate and broadcast
vital data for major earthquakes, and ultimately helping public
safety agencies respond more quickly to potential injuries and
damage (Internet 1996). Although this system is very sophisticated,
in the author's opinion it will not provide an adequate warning
to populated areas, since the same strong motion instruments are
being used as input to the system. A few seconds of warning could
be given from such a system if a large earthquake occurred a large
distance from populated areas. For instance, if it occurred 60
miles from downtown Los Angeles, and considering that the shock
wave is traveling at 2 miles per second only 30 seconds of warning
could be given, assuming that the data could be analyzed instantly.
If the Earthquake occurred beneath the city of Los Angeles no
warning at all could be given using present technology and methods.

The ideas presented in this report will have their greatest
utility for monitoring large populated areas such as the City
of Los Angeles. History has shown that the city can survive quite
large earthquakes with only minor damage when they occur at some
distance. Since strong motion seismometers are very sensitive
to background noise, they cannot be located in highly populated
areas, even if they could be located in these areas, there is
presently no way one can predict an earthquake from such limited
bandwidth instruments. They only measure the large events after
they happen and are incapable of measuring the small events that
precede them. This analogous to using a 1 mile fishing line to
catch fish. If you catch a whale you can feel it, but you could
never detect a "nibble" or a "strike" or a
small fish if it gets on the line. The higher frequencies associated
with these events are attenuated before reaching the fishing pole.

To implement such a system as proposed in this report will
not be inexpensive. The drilling of deep wells will comprise a
large percentage of the cost. Some efforts. such as "frame
relay" mentioned previously are already underway to solve
the communications problem in transferring the data to a central
location. The only other component left is to provide the high
frequency data as input to this system and the failure model software
to go with it.

SUMMARY AND CONCLUSIONS

It is shown in this report that permanent deformation processes
in rocks produce high frequency signals prior to fast fracture.
An analogy was drawn between the use of high frequency sensors
to detect Acoustic Emissions due to crack growth in materials
and man made structures and the deformation processes and crack
growth occurring in geologic materials along a fault line. It
is shown that the Acoustic Emission signals from crack growth
in man made materials can be correlated with the stress intensity
factor K at the crack tip, and inference is made that the same
correlation exists in geologic materials along a fault line. Special
emphasis is placed on using high frequency sensors in deep boreholes
in order to detect small seismic events that precede larger movements
of a fault line. It is shown that the signal to noise ratio obtainable
in a deep bore hole is excellent compared to that obtained from
surface instruments. Calculations show that frequencies of 1,700
cy/sec can be detected at a distance of 10km from a seismic event,
and at 14,000 cy/sec from events of 1km. A failure prediction
model is presented based on detecting high frequency events from
boreholes evenly spaced on a 10km grid. Data from several types
of crack propagation in man made materials are presented which
show the failure model can accurately predict failure from crack
growth in these materials. Inference is made that the same criterion
can be used to predict earthquakes in time to give hours or days
of warning.

The author wishes to express special thanks to Baxter Armstrong
for his help and advise in preparing this report, and Rachael
Abercrombie for her helpful email communications.